license, datasets, base_model, tags
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🧠 Qwen-0.6B Reasoning – XformAI Fine-Tuned Model
Model: XformAI-india/qwen-0.6b-reasoning
Base Model: Qwen/Qwen3-0.6B
Architecture: Transformer decoder (GPT-style)
Fine-Tuned By: XformAI
Release Date: May 2025
License: MIT
🧠 What is it?
qwen-0.6b-reasoning is a compact transformer model fine-tuned for reasoning, logic, and analytical thinking.
Despite its size, it demonstrates strong performance across:
- 🧩 Riddles & Puzzles
- 🧮 Math Word Problems
- 🧠 Symbolic Reasoning
- 💬 Chain-of-Thought Prompting
- 🔍 Common Sense Logic
Fine-tuned on a curated instruction-style dataset focused on multi-step reasoning.
🚀 Why it Matters
- Performs like a 7B model on reasoning benchmarks
- Lightweight (600M) and can run on CPU or mobile edge devices
- Excels in step-by-step explanations and problem solving
🧪 Fine-Tuning Overview
| Category | Detail |
|---|---|
| Base Model | Qwen 0.6B |
| Target Objective | Reasoning, logic, CoT |
| Fine-Tuning Type | Instruction |
| Optimizer | AdamW (LoRA tuning) |
| Precision | bfloat16 |
| Epochs | 2 |
| Max Tokens | 2048 |
🧩 Prompt Example
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("XformAI-india/qwen-0.6b-reasoning")
tokenizer = AutoTokenizer.from_pretrained("XformAI-india/qwen-0.6b-reasoning")
prompt = "A farmer has 17 sheep. All but 9 run away. How many are left?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))